Predicting the chance of live birth for women undergoing IVF: a novel pretreatment counselling tool
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STUDY QUESTION Which pretreatment patient variables have an effect on live birth rates following assisted conception? SUMMARY ANSWER The predictors in the final multivariate logistic regression model found to be significantly associated with reduced chances of IVF/ICSI success were increasing age (particularly above 36 years), tubal factor infertility, unexplained infertility and Asian or Black ethnicity.WHAT IS KNOWN ALREADY The two most widely recognized prediction models for live birth following IVF were developed on data from 1991 to 2007; pre-dating significant changes in clinical practice. These existing IVF outcome prediction models do not incorporate key pretreatment predictors, such as BMI, ethnicity and ovarian reserve, which are readily available now. STUDY DESIGN, SIZE, DURATION In this cohort study a model to predict live birth was derived using data collected from 9915 women who underwent IVF/ICSI treatment at any CARE (Centres for Assisted Reproduction) clinic from 2008 to 2012. Model validation was performed on data collected from 2723 women who underwent treatment in 2013. The primary outcome for the model was live birth, which was defined as any birth event in which at least one baby was born alive and survived for more than 1 month.PARTICIPANTS/MATERIALS, SETTING, METHODS Data were collected from 12 fertility clinics within the CARE consortium in the UK. Multivariable logistic regression was used to develop the model. Discriminatory ability was assessed using the area under receiver operating characteristic (AUROC) curve, and calibration was assessed using calibration-in-the-large and the calibration slope test. MAIN RESULTS AND THE ROLE OF CHANCE The predictors in the final model were female age, BMI, ethnicity, antral follicle count (AFC), previous live birth, previous miscarriage, cause and duration of infertility. Upon assessing predictive ability, the AUROC curve for the final model and validation cohort was (0.62; 95% confidence interval (CI) 0.61–0.63) and (0.62; 95% CI 0.60–0.64) respectively. Calibration-in-the-large showed a systematic over-estimation of the predicted probability of live birth (Intercept (95% CI) = −0.168 (−0.252 to −0.084), P < 0.001). However, the calibration slope test was not significant (slope (95% CI) = 1.129 (0.893–1.365), P = 0.28). Due to the calibration-in-the-large test being significant we recalibrated the final model. The recalibrated model showed a much-improved calibration. LIMITATIONS, REASONS FOR CAUTION Our model is unable to account for factors such as smoking and alcohol that can affect IVF/ICSI outcome and is somewhat restricted to representing the ethnic distribution and outcomes for the UK population only. We were unable to account for socioeconomic status and it may be that by having 75% of the population paying privately for their treatment, the results cannot be generalized to people of all socioeconomic backgrounds. In addition, patients and clinicians should understand this model is designed for use before treatment begins and does not include variables that become available (oocyte, embryo and endometrial) as treatment progresses. Finally, this model is also limited to use prior to first cycle only. WIDER IMPLICATIONS OF THE FINDINGS To our knowledge, this is the first study to present a novel, up-to-date model encompassing three readily available prognostic factors; female BMI, ovarian reserve and ethnicity, which have not previously been used in prediction models for IVF outcome. Following geographical validation, the model can be used to build a user-friendly interface to aid decision-making for couples and their clinicians. Thereafter, a feasibility study of its implementation could focus on patient acceptability and quality of decision-making.
|Early online date||25 Oct 2015|
|Publication status||Published - Jan 2016|
- prediction model, live birth, IVF, assisted conception, counselling